ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Process
Published in NeurIPS, 2024
Neural radiance fields (NeRFs) excel at 3D scene reconstruction but struggle with sparse, unconstrained camera views. ProvNeRF addresses this by modeling per-point provenance as a stochastic process, enabling improved uncertainty estimation, criteria-based view optimization, and enhanced novel view synthesis. Our method, compatible with any pre-trained NeRF, extends IMLE for stochastic processes, leveraging training camera poses to enrich the 3D point information.
Recommended citation: Nakayama, G.K., Uy, M.A., You, Y., Li, K., & Guibas, L. (2024). ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Process. NeurIPS 2024.